There are many job profiles in the Big Data world, such as Data Engineers, Data Scientists, Data Analysts, Business Analysts, etc. Out of these, Data Scientist is the most popular and sought-after one Beginners need clarification on these profiles. They need help deciding whether Data Science is a good fit , and determining the best suited resources . There are several myths surrounding Data Science. Let’s bust them. .
Myth 1 - All data roles are the same
People perceive Data Analysts, Data engineers and Data Scientists do the same thing However, their responsibilities are entirely different. The confusion arises because all these roles are under the umbrella of Big Data. The role of a Data Engineer is to work on core engineering and build scalable data pipelines so that raw data can be pulled from multiple sources, transformed and dumped into the downstream systems. Data Scientists and analysts depend on this process for the transformed data. With this, they will be able to represent meaningful insight, and will be able to create accurate Machine Learning models.
Myth 2- PhD or Master’s degree is crucial
This statement is only partially true . It will depend on the job role. For example, if you are in a research field or if you want to be an applied scientist, you need a Master’s or PhD. But suppose you want to solve complex data and work in Deep Learning/Machine Learning, you will be using Data Science elements like the libraries and Data analysis approach to create those complex data mysteries. If you are someone from a non-tech background, you can still get into the Data Science domain if you have the right skill sets.
Myth 3 - Data scientists need to be pro-coders
As a Data Scientist, your job would be to work on the data extensively. The pro-coding is to work on the competitive programming end and have strong knowledge of data structures and algorithms. Problem-solving skills should be excellent. In Data Science, there are languages like Python and R, which provide strong support for multiple libraries that can be used to solve complex data-related problems. The focus should be on how to use these libraries and their different models so that you can create the best data and machine learning models.
Myth 4 - Data science is only for graduates in technology
This is one of the most important myths . In the Data Science domain, many people are from non-tech backgrounds. Very few people are moving from the domain of computer science to the data science Companies recruit for data science and associated profiles, and many who get into these roles are from non-tech backgrounds with good problem-solving skills, aptitude and understanding of the business use cases. A suitable skill set can get someone into the data science domain irrespective of their educational background.
Myth 5 - Data science is only about predictive modelling
Data scientists spend 80 per cent of their time cleaning and transforming data, and 20% goes into data modelling. Being a data scientist, one needs clean and transformed data if one wants to create an accurate machine learning model or data model. When we are working on a big data solution, there are multiple steps involved in it. The first step is the transformation of data. The raw data contains some error-prone values and garbage records as well. To create an accurate machine learning model, we need to have meaningful transformed data.
Myth 6 - Data Science requires mathematical background
Being good in math is definitely important as a Data Scientist, while analysing the data requires mathematical concepts such as aggregating the data, statistics, probability, etc. But these are not a must to become a Data Scientist. In Data Science, we have some great programming languages like Python and R, which provide support for libraries which we can use for mathematical computations. So you don’t need to be a math expert unless you need to innovate or create an algorithm.
Myth 7 - Learning a tool is enough to become a Data Scientist
The Data Science profile combines multiple technical and non-technical skills. You must rely on something other than programming or any specific tool you think is getting used in Data Science. As we work on complex data problems, we need to interact with stakeholders and work with the business directly to get all the requirements and understand the data domain. Why are we processing it, what meaningful insights can we get from the transformed data, and how can we solve it? - are some questions that a Data Scientist works with .
Myth 8 - Companies aren’t hiring freshers
A few years ago, this statement made sense. But nowadays, freshers are self-aware and self-motivated. They are interested in exploring data science and data engineering and are putting efforts. Freshers actively participate in competitions, hackathons, open-source contributions and building projects, which help them acquire the right skill- set for the Data Science profile, allowing companies to hire freshers.
Myth 9 - Transitioning is impossible in the Data Science domain
If you come from a Data related background like Data Engineer, Data Analyst, or Business Analyst, then this transition will be easy for you. Even if you are coming from other profiles like testing or software engineering, transitioning into a data science profile is possible . You need to work on relevant skill sets on realistic projects and prove your interest and the you can add to the company.
Myth 10 - Data Science competitions will make you an expert
Data Science competitions are suitable for acquiring the right skills, to get an understanding of the Data Science environment and to build developer skills. But competitions alone will not help you become a Data Scientist. It will add value to your resume . But to become an expert, you need to work on real use cases or production-level applications. Ideally, it’s better to get internships . adding these will provide a significant weightage and who knows , you might get offers from leading companies.
The writer is Senior Vice President, Data Science & ML business at Scaler and InterviewBit